Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

This paper presents a review of deep learning (DL)-based medical image registration
methods. We summarized the latest developments and applications of DL-based registration …

In this work, we study the 1-bit convolutional neural networks (CNNs), of which both the
weights and activations are binary. While being efficient, the classification accuracy of the …

We present Habitat 3.0: a simulation platform for studying collaborative human-robot tasks in
home environments. Habitat 3.0 offers contributions across three dimensions:(1) Accurate …

Visual attention, derived from cognitive neuroscience, facilitates human perception on the
most pertinent subset of the sensory data. Recently, significant efforts have been made to …

Powered by massive curated training data Segment Anything Model (SAM) has
demonstrated its impressive generalization capabilities in open-world scenarios with the …

Large language models (LLMs) have achieved impressive pro-gress on several open-world
tasks. Recently, using LLMs to build embodied agents has been a hotspot. This paper …

This article is about deep learning (DL) and deep reinforcement learning (DRL) works
applied to robotics. Both tools have been shown to be successful in delivering data-driven …

The aspect ratio of a target changes frequently during an unmanned aerial vehicle (UAV)
tracking task, which makes the aerial tracking very challenging. Traditional trackers struggle …

Low-light image enhancement is a notoriously challenging problem. Enhancement of low-
light images is intended to increase contrast, adjust the tone, suppress noise, and produce …